PROPheT is a novel application that enables instance extraction and ontology population from Linked Data, using a user-friendly graphical user interface (GUI). In PROPheT, concepts, i.e. realisations of entities, and relations populated in online Linked Data sources (such as DBpedia) can be located, filtered and inserted into a user’s own domain ontology. 

 

PROPheT offers three types of instance extraction-related functionalities (instance-based populating, class-based populating and instance enrichment) along with user-driven mapping of data properties. It is flexible enough to work with any domain ontology (written in OWL) and any RDF Linked Data set that is available via a SPARQL endpoint. 

 

 

Features

• Three modes of instance extraction-related functionalities (instance-based populating, class-based populating and instance enrichment).
• User-driven mapping of data properties.
• Importing a domain ontology (over HTTP and locally).
• Exporting the populated ontology in the most popular formats (.owl, .rdf, .ttl, .nt and .n3.).
• Flexibility to seamlessly work with any domain ontology (written in OWL) and any RDF Linked Data set available via a SPARQL endpoint.
• Elimination of redundancy in the instance set by handling duplicates.
• User-friendly GUI with enriched display of content and information, as well as useful function utilities for the user. 

 

Relevant Publications

• Mitzias, P., Riga, M., Kontopoulos, E., Stavropoulos, T. G., Andreadis, S., Meditskos, G., & Kompatsiaris, I. (2016, September). User-Driven Ontology Population from Linked Data Sources. In: 7th International Conference on Knowledge Engineering and the Semantic Web (KESW 2016). pp. 31–41. Springer International Publishing, Prague, Czech Republic [paper].
• Kontopoulos, E., Mitzias, P., Riga, M., Kompatsiaris, I. (2017). A Domain-Agnostic Tool for Scalable Ontology Population and Enrichment from Diverse Linked Data Sources. In: Kalinichenko, L.A., Manolopoulos, Y., Skvortsov, N.A., and Sukhomlin, V.A. (eds.) Data Analytics and Management in Data Intensive Domains: Collection of Scientific Papers of the XIX International Conference DAMDID / RCDL’2017. pp. 234–240. Moscow: FRC CSC RAS, Moscow, Russia [paper, slides].

 

Downloads

• Download the tool here
• Official PROPheT documentation here

 

Acknowledgements

This work was supported by the following EU Projects:

• PERICLES (FP7 - No. 601138)
• DigiArt (Horizon 2020 - No. 601138)

 

Contact

• Mr. Panagiotis Mitzias
• Ms. Marina Riga
• Dr. Efstratios Kontopoulos
• Dr. Ioannis Kompatsiaris